FiPddlmZddlZddlZddlZddlZddlZddlmZddl m Z m Z ddl m Z ddlZddlZddlmZddlmZmZmZmZmZmZmZddlmZdd lmZmZdd l m!Z!dd Z"dd Z# d dd Z$ddZ%ddZ&ddZ'ddZ(dddZ)dZ*d d!dZ+d"dZ,d#d$dZ-y)%) annotationsN) defaultdict)ThreadPoolExecutor as_completed)Path)Image) ASSETS_URL DATASETS_DIRLOGGER NUM_THREADSTQDMYAML clean_url) check_file)download zip_directory)increment_pathc gdS)aConvert 91-index COCO class IDs to 80-index COCO class IDs. Returns: (list[int | None]): A list of 91 elements where the index represents the 91-index class ID and the value is the corresponding 80-index class ID, or None if there is no mapping. )[r N NNN !"#$%&'N()*+,-./0123456789:;N<NN=N>?@ABCDEFGHNIJKLMNONrd\/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/data/converter.pycoco91_to_coco80_classrgs\ \rec gdS)aConvert 80-index (val2014) to 91-index (paper). Returns: (list[int]): A list of 80 class IDs where each value is the corresponding 91-index class ID. Examples: >>> import numpy as np >>> a = np.loadtxt("data/coco.names", dtype="str", delimiter="\n") >>> b = np.loadtxt("data/coco_paper.names", dtype="str", delimiter="\n") Convert the darknet to COCO format >>> x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] Convert the COCO to darknet format >>> x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] References: https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ )Prrrrrrrrrrrr!r"r#r$r%r&r'r(r)r*r+r,r-r/r0r3r4r5r6r7r8r9r:r;r<r=r>r?r@rBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQrRrSrTrUrWrZr\r]r^r_r`rarbrcPQRTUVWXYZrdrdrerfcoco80_to_coco91_classrs~s(Q Qrec  t|}|dz |dz fD]}|jddt}tt |j j dD] }|rdn|jjdd} t |dz | z } | jdd|r,| dz jdd| d z jddt|d 5} tj| } d d d  dD cic] } | d d|  }} tt}| dD]}||dj|g}t|j d|D]\}}||d}|d|d}}|r&t#t |dj%dn|d} |r&|jt#t d| z g}g}g}|D]|}|j'ddrt)j*|dt(j,}|d dxxx|dd dz z ccc|ddgxx|zcc<|dd gxx|zcc<|ddks|d dkr|r ||d!dz n|d!dz }|g|j/}||vs|r|j'd"|j|t)j*|d"j1d#d t)j*||dgz j1d#j/z|j||sH|j'd$}|t3|dk(r|jg|t3|dkDrmt5|}t)j6|d%t)j*||gz j1d#j/}|j|g||Dcgc] }|D]}| }}}t)j*|j1d#dt)j*||gz j1d#j/}|j|g|t| | z j9d&d'd 5} t;t3|D]^}|r g||}!n g|rt3||dkDr||n||}!| j=d(t3|!zj?|!zd)z` d d d |st ||j@jd*djd+d&z }"t|"d'd 5} | jCd,|Dd d d tEjF|rd-nd.d/|j y #1swYxYwcc} wcc}}w#1swYTxYw#1swYkxYw)0aConvert COCO dataset annotations to a YOLO annotation format suitable for training YOLO models. Args: labels_dir (str, optional): Path to directory containing COCO dataset annotation files. save_dir (str, optional): Path to directory to save results to. use_segments (bool, optional): Whether to include segmentation masks in the output. use_keypoints (bool, optional): Whether to include keypoint annotations in the output. cls91to80 (bool, optional): Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs. lvis (bool, optional): Whether to convert data in lvis dataset way. Examples: >>> from ultralytics.data.converter import convert_coco Convert COCO annotations to YOLO format >>> convert_coco("coco/annotations/", use_segments=True, use_keypoints=False, cls91to80=False) Convert LVIS annotations to YOLO format >>> convert_coco("lvis/annotations/", use_segments=True, use_keypoints=False, cls91to80=False, lvis=True) labelsimagesTparentsexist_okz*.json instances_ train2017val2017utf-8encodingNiddrimage_idz Annotations descheightwidthcoco_urlzhttp://images.cocodataset.org file_namez./imagesiscrowdFbbox)dtyperrrr category_id keypoints segmentationaxis.txta%g  lvis_v1_z.jsonc3&K|] }|d ywrNrd).0lines rf zconvert_coco..Ys?TvR[?LVISCOCOz/ data converted successfully. Results saved to )$rmkdirrgsortedrresolveglobstemreplaceopenjsonloadrlistappendr itemsstr relative_togetnparrayfloat64tolistreshapelenmerge_multi_segment concatenate with_suffixrangewriterstripname writelinesr info)# labels_dirsave_dir use_segments use_keypoints cls91to80lvispcoco80 json_filelnamefnfdataxrvrann image_txtimg_idannsimghwbboxessegmentsrboxclssegsijfilerfilenames# rf convert_cocors8h'H  (X"5 5- t,-$ %FD,446;;HEFO@   6 6|R H (^h & . - +  $ $TD $ A )^ " "4$ " ? )g . !99QN!N W WXZ [ b b dA$OOSI1I6A 7FrAv**62C'J Kds6{+KA$1)A,1 -9c(1+>NQR>RhqkX^_`Xa JJD 199;dBTIJK K K[5 Kn H~ (>(>z2(N(V(VW^`f(ggHhg6 @! ?Y?? @ @]O@b KKT6v..^_g_o_o_q^rstQ  =`!<  K K @ @s1,V%V2$V7 A6V=W %V/ =W W c t|Dcic]}|dz| }}t|jD]*}|jdvst j t |tj}|j\}}tjd|d|d|tj|} g} | D]} | dk(r |j| d} | dk(rtjd| d |d >t j|| k(j!tj"tj$tj&\} }| D]~}t)|d k\s|j+}| g}|D]D}|j-t/|d|z d |j-t/|d|z d F| j-|t||j0d z }t3|dd5}| D]5}dj5t7t |}|j9|dz7 dddtjd|d|d|-ycc}w#1swY0xYw)uConvert a dataset of segmentation mask images to the YOLO segmentation format. This function takes the directory containing the binary format mask images and converts them into YOLO segmentation format. The converted masks are saved in the specified output directory. Args: masks_dir (str): The path to the directory where all mask images (png, jpg) are stored. output_dir (str): The path to the directory where the converted YOLO segmentation masks will be stored. classes (int): Total number of classes in the dataset, e.g., 80 for COCO. Examples: >>> from ultralytics.data.converter import convert_segment_masks_to_yolo_seg The classes here is the total classes in the dataset, for COCO dataset we have 80 classes >>> convert_segment_masks_to_yolo_seg("path/to/masks_directory", "path/to/output/directory", classes=80) Notes: The expected directory structure for the masks is: - masks ├─ mask_image_01.png or mask_image_01.jpg ├─ mask_image_02.png or mask_image_02.jpg ├─ mask_image_03.png or mask_image_03.jpg └─ mask_image_04.png or mask_image_04.jpg After execution, the labels will be organized in the following structure: - output_dir ├─ mask_yolo_01.txt ├─ mask_yolo_02.txt ├─ mask_yolo_03.txt └─ mask_yolo_04.txt r>.jpg.png Processing z imgsz = z x rrzUnknown class for pixel value z in file z , skipping.rrrrr~r rNzProcessed and stored at )rriterdirsuffixcv2imreadrIMREAD_GRAYSCALEshaper rruniquerwarning findContoursastypeuint8 RETR_EXTERNALCHAIN_APPROX_SIMPLErsqueezerroundrrjoinmapr) masks_dir output_dirclassesrpixel_to_class_mapping mask_pathmask img_height img_width unique_valuesyolo_format_datavalue class_indexcontours_contour yolo_formatpoint output_pathritemrs rf!convert_segment_masks_to_yolo_segr^sjD16g?1a!eQh??)_,,.%e   / /::c)nc.B.BCD$(JJ !J KK+i[ *S T UIIdOM! & =A:488C "$NN%CE7)T]S^^i#jk"..U]**2884c6G6GI`I` ! (=G7|q(")//"3'2m %,PE'..uU1X 5I1/MN'..uU1X 5JA/NOP)// <= =.z* /?t-DDKk39 ,T,,D88CTN3DJJtd{+, , KK2;-y TWXaWbc dK%e@D , ,s I%?;I**I3 c  t|}iddddddddd d d d d ddddddddddddddddddd d!d"d#d$i d1 fd% }d&D]}|d'z |z }|d(z |d)z }|d(z |z }|jd*d*+t|j}t |d,|d-.D]Z}|j d/k7r|j }tjt|} | jd0d\} } ||| | ||\y0)2u Convert DOTA dataset annotations to YOLO OBB (Oriented Bounding Box) format. The function processes images in the 'train' and 'val' folders of the DOTA dataset. For each image, it reads the associated label from the original labels directory and writes new labels in YOLO OBB format to a new directory. Args: dota_root_path (str): The root directory path of the DOTA dataset. Examples: >>> from ultralytics.data.converter import convert_dota_to_yolo_obb >>> convert_dota_to_yolo_obb("path/to/DOTA") Notes: The directory structure assumed for the DOTA dataset: - DOTA ├─ images │ ├─ train │ └─ val └─ labels ├─ train_original └─ val_original After execution, the function will organize the labels into: - DOTA └─ labels ├─ train └─ val planershiprz storage-tankrzbaseball-diamondrz tennis-courtrzbasketball-courtrzground-track-fieldrharborrbridgerz large-vehiclerz small-vehicler helicopterr roundaboutr zsoccer-ball-fieldr!z swimming-poolr"zcontainer-craner#airportr$helipadr%c z||dz }||dz }|jd5}|jd5}|j} | D]} | jj} t | dkr0| d} | } | ddDcgc] }t |}}t dDcgc]}|dzdk(r|||z n|||z }}|Dcgc]}|d }}|j| d d j|d  ddddddycc}wcc}wcc}w#1swY!xYw#1swYyxYw) zaConvert a single image's DOTA annotation to YOLO OBB format and save it to a specified directory.rrrrrNrrz.6grr) r readlinesstripsplitrfloatrrr) image_name image_width image_heightorig_label_dirrorig_label_path save_pathrglinesrparts class_name class_idxrcoordsrnormalized_coordscoordformatted_coords class_mappings rf convert_labelz/convert_dota_to_yolo_obb..convert_labels|(j\+>>*T22  ! !# & G!Y^^C-@ GAKKME G **,u:>"1X )*5 ,1"1I6q%(66afghai%\]q1uzF1I +vayvaltrainrvru _originalTrwrz imagesrrN) rrrintrr+rrrr) rrrrr rrrrrr) dota_root_pathr'phase image_dirrr image_paths image_pathimage_name_without_extrrrr&s @rfconvert_dota_to_yolo_obbr2s>.)N  A    A  a ! !   b b R  2!" 2#$ 2%M*G("R"X-5 '(2wi5HH!H,u4td39,,./ {;ugW1MN RJ  F*%/__ "**S_-C99Ra=DAq 0!Q Q  RRrec|dddddf|dddddfz dzjd}tjtj|d|jS)aFind a pair of indexes with the shortest distance between two arrays of 2D points. Args: arr1 (np.ndarray): A NumPy array of shape (N, 2) representing N 2D points. arr2 (np.ndarray): A NumPy array of shape (M, 2) representing M 2D points. Returns: (tuple[int, int]): A tuple (idx1, idx2) where idx1 is the index in arr1 and idx2 is the index in arr2 of the pair with the shortest distance. Nrrr)sumr unravel_indexargminr)arr1arr2diss rf min_indexr:sV D! tD!QJ/ /A 5 : :2 >C  BIIc5syy AArec.g}|Dcgc]'}tj|jdd)}}tt |Dcgc]}g}}tdt |D]E}t ||dz ||\}}||dz j |||j |GtdD]I}|dk(rt|D]\}}t |dk(r%|d|dkDr|ddd}||dddddf||<tj|||d d||<tj||||ddg||<|dt |dz hvr|j ||d|d|dz g}|j |||d|ddztt |dz ddD]E}|dt |dz hvs||}t|d|dz } |j ||| dGL|Scc}wcc}w)aMerge multiple segments into one list by connecting the coordinates with the minimum distance between each segment. This function connects these coordinates with a thin line to merge all segments into one. Args: segments (list[list]): Original segmentations in COCO's JSON file. Each element is a list of coordinates, like [segmentation1, segmentation2,...]. Returns: (list[np.ndarray]): A list of connected segments represented as NumPy arrays. rrrrNr) rrrrrr:r enumeraterollrabs) rrrridx_listidx1idx2kidxnidxs rfrrsD A4<=q ##B*=H=!#h-01q1H11c(m $!xA < dQt$ 4 ! 1X1 6#H- ?3s8q=SVc!f_dd)C"*1+ddAg"6HQK gghqkCF7C  nnhqk8A;r?-KL CMA-..HHXa[)c!fs1vo.CHHXa[Q#a&1*=> ? 3x=1,b"5 1QH  122"1+Cs1vA/DHHXa[/0  1'10 HE>1s ,H  Hc ddlm}ddlm}ddlm}||t ttdd}t|jdd dkDrtjd y tjd ||}t|jt|jd D]}|d\} } |d} t| dk(r| d d ddgfxx| zcc<| d d ddgfxx| zcc<tj|d} || || dd|} | dj j"|d <|r t%|nt%|j&dz }|j)dd|jD]}g}t%|dj+dj,}||z }|d}t/|d D]c\}}t|dk(rt1||g|j3d}|j5dt|zj7|zet9|dd5}|j;d |Dd d d tjd!|y #1swYxYw)"uConvert existing object detection dataset (bounding boxes) to segmentation dataset in YOLO format. Generates segmentation data using SAM auto-annotator as needed. Args: im_dir (str | Path): Path to image directory to convert. save_dir (str | Path, optional): Path to save the generated labels, labels will be saved into `labels-segment` in the same directory level of `im_dir` if save_dir is None. sam_model (str): Segmentation model to use for intermediate segmentation data. device (int | str, optional): The specific device to run SAM models. Notes: The input directory structure assumed for dataset: - im_dir ├─ 001.jpg ├─ ... └─ NNN.jpg - labels ├─ 001.txt ├─ ... └─ NNN.txt r)SAM) YOLODataset) xywh2xyxyir)nameschannels)rrz;Segmentation labels detected, no need to generate new ones!NzBDetection labels detected, generating segment labels by SAM model!zGenerating segment labelstotalrrrrrim_fileF)rverbosesavedevicezlabels-segmentTrwrrrrrr~rc3&K|] }|dz ywrrd)rtexts rfrz$yolo_bbox2segment..s77rz"Generated segment labels saved in ) ultralyticsrFultralytics.datarGultralytics.utils.opsrHdictrrrrur rr rrmasksxynrparentrrrr<r+rrrrr)im_dirr sam_modelrPrFrGrHdatasetlabelrrboxesim sam_resultstextslb_nametxt_filerrrrrs rfyolo_bbox2segmentrdGsc0 ,/&t$uT{2Ca'PQG 7>>! Z ()A- QR KKTUIIgnnC,?Fab 5W~1h u:?  a!QiA a!QiA ZZi( )9U+;UQV_ef 'N0044j 5"*tH~tF|/B/BEU/UH NN4$N/ 8uY'(44V<AAg%EleJ/0 >DAq1v{AK0!))B-0D LL%#d)+335< =  > (C' 2 8a LL77 7 8 8 8 KK4XJ?@ 8 8s I<<J c dd}tdz }ttdg|jt j |dz dz dt t 5}d D]}|d z |z }|jdd ||d z }|jr~t|d5}|Dcgc]}||jz }}dddD cgc]} |j|| } } tt| t| d|D]} t!j"d|d|d dddt!j$dycc}w#1swYxYwcc} w#1swY5xYw)aCreate a synthetic COCO dataset with random images based on filenames from label lists. This function downloads COCO labels, reads image filenames from label list files, creates synthetic images for train2017 and val2017 subsets, and organizes them in the COCO dataset structure. It uses multithreading to generate images efficiently. Examples: >>> from ultralytics.data.converter import create_synthetic_coco_dataset >>> create_synthetic_coco_dataset() Notes: - Requires internet connection to download label files. - Generates random RGB images of varying sizes (480x480 to 640x640 pixels). - Existing test2017 directory is removed as it's not needed. - Reads image filenames from train2017.txt and val2017.txt files. c J|jstjddtjddf}tjd|tjddtjddtjddfj |yy)zcGenerate a synthetic image with random size and color for dataset augmentation or testing purposes.iiRGBr)sizecolorN)existsrandomrandintrnewrO) image_fileris rfcreate_synthetic_imagez=create_synthetic_coco_dataset..create_synthetic_images~  "NN3,fnnS#.FGD II~~a-v~~a/Ev~~VWY\G]^ d: #recocoz/coco2017labels-segments.zip)dirrutest2017T ignore_errors) max_workers>r}r|rvrwrr~rNzGenerating images for rKz Labels file z- does not exist. Skipping image creation for .z,Synthetic COCO dataset created successfully.)ror)r rr rYshutilrmtreerr rrkrrsubmitr rrr rr) rprrexecutorsubset subset_dirlabel_list_filerr image_filesrofuturesrs rfcreate_synthetic_coco_datasetrs$  C  89 : K MM#.:-TB  4w. wFx&0J   TD  9"vhdO3O%%'/G<EBC"D$3#5"DK"DEbmmS]8??+A:Nmml733w>klrksstuv ww$ KK>?#EEEnwwsDAE$ E%E ?E E$ E&A E$EE  E$$E-cddlm}ddlm}t |}|j rd|ddhz Dcgc]}|j d|D]}|}}}|D]!} t| ||r| j#|r t|y y |jd } tjtj t#|tj$} t'j(gd } t'j*d d |}|| j,| ddd}||}tj.t#| t'j0|ddj3t&j4j7dddtjd| y cc}}w#t$r&} tjd| d| Yd } ~ |d } ~ wwxYw)aKConvert RGB images to multispectral images by interpolating across wavelength bands. This function takes RGB images and interpolates them to create multispectral images with a specified number of channels. It can process either a single image or a directory of images. Args: path (str | Path): Path to an image file or directory containing images to convert. n_channels (int): Number of spectral channels to generate in the output image. replace (bool): Whether to replace the original image file with the converted one. zip (bool): Whether to zip the converted images into a zip file. Examples: Convert a single image >>> convert_to_multispectral("path/to/image.jpg", n_channels=10) Convert a dataset >>> convert_to_multispectral("coco8", n_channels=10) r)interp1d) IMG_FORMATStiftiffz*.zError converting : Nz.tiff)iiiiilinearF extrapolate)kind bounds_error fill_valuerhrrz Converted )scipy.interpolaterultralytics.data.utilsrris_dirrglobconvert_to_multispectralunlink Exceptionr rrrrcvtColorrr COLOR_BGR2RGBrrlinspaceT imwritemulticliprr transpose)path n_channelsrziprrextrim_filesim_patherrrgb_wavelengthstarget_wavelengths multispectrals rfrrs&+2 :D {{}"-"?a#$**WYZ]Y^U_J`aQAaAaa @G @(*=NN$  @  $  &&w/ ll3::c$i0#2C2CD((?3[[c:> _&&([h i,-  [)277=!S+I+P+PQSQYQY+Z+d+defhikl+mn j ./-b  @ /y1#>?? @s"F%F++ G4GGcg}g}|D]}|jdijdgD]J}t|dz }|dkDr%|j||j|ddt|dk\sJnt|dk\sn|rtt|dk7r t d|d}|d zdk(rt d |Dr|d zd gS|d zdk(r|d zdk7r|d zd gSt d) aInfer kpt_shape [num_keypoints, dims] from NDJSON pose annotations. Scans up to 50 pose annotations across image records. Annotation format is [classId, cx, cy, w, h, kp1_x, kp1_y, kp1_vis, ...] so keypoint values start at index 5. Tries dims=3 first (x, y, visibility) with visibility validation ({0, 1, 2}), then falls back to dims=2 (x, y only) when values are unambiguously not divisible by 3. rposerrNrFrzZPose dataset missing required 'kpt_shape'. See https://docs.ultralytics.com/datasets/pose/rc3>K|]}|dddD]}|dv yw)rNr)rrrrd)rrvs rfrz*_infer_ndjson_kpt_shape.. s*KQ1QTT7Ka!y.K.Ksr)rrrset ValueErrorall) image_records kpt_lengthssamplesrecordrkpt_lenns rf_infer_ndjson_kpt_shapersKG ::mR044VR@ C#hlG{""7+s12w';2%   { r !   #c+./14uvvAA 1uzcK'KKQ{ 1uza!eqjQ{ q rrrec  '()*+,-./0Kddlm}|dddl(tt |}t|xst }t |5}|Dcgc]6}|jstj|j8}}dddd|dd}}tj}|D]} | jD cic]\} } | dk7s | | } } } | jdrE| jdrt| dn"t|j j#| d<|j%tj&| d j)|j+dd } ||j,d | z ++d z }|j/rA t1j2|))jd| k(rt5)+fddDr|S|Dchc]}|d }}|jddk(,|jdijD cic]\} } t9| | c} } *d}|jdd},s|Dchc]>}|jdij;D]}|D]}|rt9|d@}}}}|s*rGt=|t?*z}*r)tA|dzD]}*jC|d|n|dz},sd|vrtEdtG|d|vr|D cgc]} | jddk(s| }} tI|dkrtEdtI|dtKjLdjO|t=dtI|dz}|d|D]} d| d< |jQdtSjTdtI|d tI||z d!|d"|d#k(rd$|vrtW||d$<+jY''r.|j[d %,st]j^+d&z d '+jad d (d},stc|}*r*|d)<n|||d*<|jedd|jed+dtG|D]<}+d,z |z jad d (+d&z |z jad d (d-|||<>'(*+,fd..tgjhtkd/tI|/(jm4d{0totI|d0|jpd1+d2tI|d34--./0fd5}tgjr|Dcgc] }|| c}d{}-judddd{twd6D}|dk(rtyd7|d8|tI|kr'tSjTd9|d:tI|d;|'rt?}|D]} | d| d}!} ,ra| jdi}"|"jdaConvert NDJSON dataset format to Ultralytics YOLO dataset structure. This function converts datasets stored in NDJSON (Newline Delimited JSON) format to the standard YOLO format. For detection/segmentation/pose/obb tasks, it creates separate directories for images and labels. For classification tasks, it creates the ImageNet-style {split}/{class_name}/ folder structure. It supports parallel processing for efficient conversion of large datasets and can download images from URLs. The NDJSON format consists of: - First line: Dataset metadata with class names, task type, and configuration - Subsequent lines: Individual image records with annotations and optional URLs Args: ndjson_path (str | Path): Path to the input NDJSON file containing dataset information. output_path (str | Path | None, optional): Directory where the converted YOLO dataset will be saved. If None, uses the DATASETS_DIR directory. Defaults to None. Returns: (Path): Path to the generated data.yaml file (detection) or dataset directory (classification). Examples: Convert a local NDJSON file: >>> yaml_path = await convert_ndjson_to_yolo("dataset.ndjson") >>> print(f"Dataset converted to: {yaml_path}") Convert with custom output directory: >>> yaml_path = await convert_ndjson_to_yolo("dataset.ndjson", output_path="./converted_datasets") Use with YOLO training >>> from ultralytics import YOLO >>> model = YOLO("yolo26n.pt") >>> model.train(data="https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-ndjson.ndjson") r)check_requirementsaiohttpNrurlr_sourceT) sort_keysr-z data.yamlhashc3K|]6}|vr0|z jxrdz |z j8yw)ruN)r)rrcached dataset_dirs rfrz)convert_ndjson_to_yolo..SsN3F?ve},446dK(red D]`}||k(r r |z z |z n dz |z |z }|js2| jjdd|j| n| js|jdx}r| jjddtdD]}d} |j|jd4d{}|j| j|jd{dddd{dddd{y dddd{y7Scc} w7x7G75#1d{7swYExYw7;#j $r[}|}|j"dvrA|j"dkr2t%j&d|d|Yd}~dddd{7yYd}~nod}~wj(t*j,f$r }|}Yd}~nFd}~wt.$r7}t%j&d|d|Yd}~dddd{7yd}~wwxYw|dkr#t+j0d|zd{7t%j&d|d|dddd{7y7U#1d{7swYyxYww)z/Process single image record with async session.NrrrclassificationrrvrurrrrzrTrwrrr2)rL)timeout>izFailed to download rFzFailed to save rz after 3 attempts: )rrrrrr write_textrkrYrrenamer ClientTimeoutraise_for_status write_bytesreadClientResponseErrorstatusr r ClientErrorasyncio TimeoutErrorrsleep)session semaphorerr original_namer class_idsclass_idr r0 label_pathlines_to_writekeyrr candidatehttp_urlattempterrorresponser_reuserrris_classifications rfprocess_recordz.convert_ndjson_to_yolo..process_records< < #)'?F6N=E **]B7K +KOO,__ !#&CKVWZK[%\4chhs3~&>%\N%\%%.dii&?$&F^`a$$&5 ":$ 1)1_z9MI"-"81"<}"L" %++-&--33D43P%,,Z8! ""((*FJJu.tracked_processs.)'9fEEF KKNMFs +)+c3&K|] }|sd yw)rNrd)rrs rfrz)convert_ndjson_to_yolo..s0aa0sz#Failed to download any images from z$. Check network connection and URLs.z Downloaded /z images from r*)?ultralytics.utils.checksrrrrr rrrloadshashlibsha256rrrrrYrrdumpsencode hexdigestris_filerrrrr+valuesmaxrr setdefaultrrrrlRandomshuffleaddr rrrkrrxryrrVpopr Semaphoremin ClientSessionr rgathercloser4 RuntimeErrorrrO)1 ndjson_pathrrrrrdataset_recordr_hrrBr hash_record_hash yaml_pathrsplits inferred_ncrrur]r max_class_idr train_records val_count data_yamlrrresults success_countexpected_pathsrrrcidscidimg_rootrrrrrrrrrrrs1 @@@@@@@@@@rfconvert_ndjson_to_yolorsB<y!z+./K{2l3K k Ia67Hd4::<DJJL)HHI$)!HeABiMN  B D() @1Q%Zq!t@ @ 55=<=EE%LYqx%8cR]RdRdRlRlRnNoK " $**[D9@@BC D LLN2A E;#3#3"4AeW ==Kk)I YYy)Fzz&!U*s3530 ! -: :&fWo :F :'**62j@)7););M2)N)T)T)VWA3q619WKK   fh /D (   **]B7>>@    aM      y3{+;;>B!!#c3}+=">?I$$&  'm${//0k]"SEWDXX`a     }(]V)@(]^^   0700M@ Mqrsss=))]O1S5G4H VaUbcd FAj!F)tA eeM2.ww/4!%d1g1"";?[__S#c(5S#SVZ#Z["";#9A#=#DE F#4;+:P$ Ayy{q6  " & )Y'aIIIA&    :X  $TX )^^    sA^\%\ ,%\ \%>^ \2\2$C ^0?\8/^4 ]9^9]  ^/A]2A4^&]]G3^7]8^;A]- ]" ]- ]'!]-5 ^]*D&^(^-3^ \%%\/*^8 ]^]^"]-*^-^3]6 4^;^)returnz list[int])z../coco/annotations/zcoco_converted/FFTF) rrrrrboolrrrrrr)rrrrrr+)r,r)r7 np.ndarrayr8r)rz list[list])Nzsam_b.ptN)rZ str | Pathrstr | Path | Noner[r)rFF)rrrr+rrrr)rrrr)N)rrrrrr). __future__rrrrrlrx collectionsrconcurrent.futuresrrpathlibrrnumpyrPILrultralytics.utilsr r r r r rrrrultralytics.utils.downloadsrrultralytics.utils.filesrrgrsrrr2r:rrdrrrrrdrerfr)s# #? bbb/?2cLeR-% uuuuuuuu uu  uu  uupHeVYRx B0 f=A@4@n00f#sLyre